An integrated approach reveals how lipo-chitooligosaccharides interact with the lysin motif receptor-like kinase MtLYR3.

N-acetylglucosamine containing compounds acting as pathogenic or symbiotic signals are perceived by plant-specific Lysin Motif Receptor-Like Kinases (LysM-RLKs). The molecular mechanisms of this perception are not fully understood, notably those of lipo-chitooligosaccharides (LCOs) produced during root endosymbioses with nitrogen-fixing bacteria or arbuscular mycorrhizal fungi. In Medicago truncatula, we previously identified the LysM-RLK LYR3 (MtLYR3) as a specific LCO-binding protein. We also showed that the absence of LCO binding to LYR3 of the non-mycorrhizal Lupinus angustifolius, (LanLYR3), was related to LysM3, which differs from that of MtLYR3 by several amino acids and, particularly, by a critical tyrosine residue absent in LanLYR3. Here, we aimed to define the LCO binding site of MtLYR3 by using molecular modelling and simulation approaches, combined with site-directed mutagenesis and LCO binding experiments. 3D models of MtLYR3 and LanLYR3 ectodomains were built, and homology modelling and molecular dynamics (MD) simulations were performed. Molecular docking and MD simulation on the LysM3 identified potential key residues for LCO binding. We highlighted by steered MD simulations that in addition to the critical tyrosine, two other residues were important for LCO binding in MtLYR3. Substitution of these residues in LanLYR3-LysM3 by those of MtLYR3-LysM3 allowed the recovery of high-affinity LCO binding in experimental radioligand-binding assays. An analysis of selective constraints revealed that the critical tyrosine has experienced positive selection pressure and is absent in some LYR3 proteins. These findings now pave the way to uncover the functional significance of this specific evolutionary pattern.

Computational Design of Miniprotein Binders.

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.

The covalent complex of Jo-In results from a long-lived, non-covalent intermediate state with near-native structure.

Covalent protein complexes have been used to assemble enzymes in large scaffolds for biotechnology purposes. Although the catalytic mechanism of the covalent linking of such proteins is well known, the recognition and overall structural mechanisms driving the association are far less understood but could help further functional engineering of these complexes. Here, we study the Jo-In complex by NMR spectroscopy and molecular modelling. We characterize a transient non-covalent complex, with structural elements close to those in the final covalent complex. Using site specific mutagenesis, we further show that this non-covalent association is essential for the covalent complex to form.

Protein Design with Deep Learning.

Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.

A tripartite carbohydrate-binding module to functionalize cellulose nanocrystals.

The development of protein and microorganism engineering have led to rising expectations of biotechnology in the design of emerging biomaterials, putatively of high interest to reduce our dependence on fossil carbon resources. In this way, cellulose, a renewable carbon based polysaccharide and derived products, displays unique properties used in many industrial applications. Although the functionalization of cellulose is common, it is however limited in terms of number and type of functions. In this work, a Carbohydrate-Binding Module (CBM) was used as a central core to provide a versatile strategy to bring a large diversity of functions to cellulose surfaces. CBM3a from Clostridium thermocellum, which has a high affinity for crystalline cellulose, was flanked through linkers with a streptavidin domain and an azide group introduced through a non-canonical amino acid. Each of these two extra domains was effectively produced and functionalized with a variety of biological and chemical molecules. Structural properties of the resulting tripartite chimeric protein were investigated using molecular modelling approaches, and its potential for the multi-functionalization of cellulose was confirmed experimentally. As a proof of concept, we show that cellulose can be labelled with a fluorescent version of the tripartite protein grafted to magnetic beads and captured using a magnet.

Computer-aided engineering of a branching sucrase for the glucodiversification of a tetrasaccharide precursor of S. flexneri antigenic oligosaccharides.

Enzyme engineering approaches have allowed to extend the collection of enzymatic tools available for synthetic purposes. However, controlling the regioselectivity of the reaction remains challenging, in particular when dealing with carbohydrates bearing numerous reactive hydroxyl groups as substrates. Here, we used a computer-aided design framework to engineer the active site of a sucrose-active [Formula: see text]-transglucosylase for the 1,2-cis-glucosylation of a lightly protected chemically synthesized tetrasaccharide, a common precursor for the synthesis of serotype-specific S. flexneri O-antigen fragments. By targeting 27 amino acid positions of the acceptor binding subsites of a GH70 branching sucrase, we used a RosettaDesign-based approach to propose 49 mutants containing up to 15 mutations scattered over the active site. Upon experimental evaluation, these mutants were found to produce up to six distinct pentasaccharides, whereas only two were synthesized by the parental enzyme. Interestingly, we showed that by introducing specific mutations in the active site of a same enzyme scaffold, it is possible to control the regiospecificity of the 1,2-cis glucosylation of the tetrasaccharide acceptor and produce a unique diversity of pentasaccharide bricks. This work offers novel opportunities for the development of highly convergent chemo-enzymatic routes toward S. flexneri haptens.

Seven Amino Acid Types Suffice to Create the Core Fold of RNA Polymerase.

The extant complex proteins must have evolved from ancient short and simple ancestors. The double-ψ ß-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its plausible evolutionary pathway started by "interlacing homodimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

Three ParA Dimers Cooperatively Assemble on Type Ia Partition Promoters.

Accurate DNA segregation is essential for faithful inheritance of genetic material. In bacteria, this process is mainly ensured by partition systems composed of two proteins, ParA and ParB, and a centromere site. Auto-regulation of Par operon expression is important for efficient partitioning and is primarily mediated by ParA for type Ia plasmid partition systems. For the F-plasmid, four ParAF monomers were proposed to bind to four repeated sequences in the promoter region. By contrast, using quantitative surface-plasmon-resonance, we showed that three ParAF dimers bind to this region. We uncovered that one perfect inverted repeat (IR) motif, consisting of two hexamer sequences spaced by 28-bp, constitutes the primary ParAF DNA binding site. A similar but degenerated motif overlaps the former. ParAF binding to these motifs is well supported by biochemical and modeling analyses. Molecular dynamics simulations predict that the winged-HTH domain displays high flexibility, which may favor the cooperative ParA binding to the promoter. We propose that three ParAF dimers bind cooperatively to overlapping motifs, thus covering the promoter region. A similar organization is found on closely related and distant plasmid partition systems, suggesting that such promoter organization for auto-regulated Par operons is widespread and may have evolved from a common ancestor.

A Comparative Study to Decipher the Structural and Dynamics Determinants Underlying the Activity and Thermal Stability of GH-11 Xylanases.

With the growing need for renewable sources of energy, the interest for enzymes capable of biomass degradation has been increasing. In this paper, we consider two different xylanases from the GH-11 family: the particularly active GH-11 xylanase from Neocallimastix patriciarum, NpXyn11A, and the hyper-thermostable mutant of the environmentally isolated GH-11 xylanase, EvXyn11TS. Our aim is to identify the molecular determinants underlying the enhanced capacities of these two enzymes to ultimately graft the abilities of one on the other. Molecular dynamics simulations of the respective free-enzymes and enzyme-xylohexaose complexes were carried out at temperatures of 300, 340, and 500 K. An in-depth analysis of these MD simulations showed how differences in dynamics influence the activity and stability of these two enzymes and allowed us to study and understand in greater depth the molecular and structural basis of these two systems. In light of the results presented in this paper, the thumb region and the larger substrate binding cleft of NpXyn11A seem to play a major role on the activity of this enzyme. Its lower thermal stability may instead be caused by the higher flexibility of certain regions located further from the active site. Regions such as the N-ter, the loops located in the fingers region, the palm loop, and the helix loop seem to be less stable than in the hyper-thermostable EvXyn11TS. By identifying molecular regions that are critical for the stability of these enzymes, this study allowed us to identify promising targets for engineering GH-11 xylanases. Eventually, we identify NpXyn11A as the ideal host for grafting the thermostabilizing traits of EvXyn11TS.

Molecular flexibility in computational protein design: an algorithmic perspective.

Computational protein design (CPD) is a powerful technique for engineering new proteins, with both great fundamental implications and diverse practical interests. However, the approximations usually made for computational efficiency, using a single fixed backbone and a discrete set of side chain rotamers, tend to produce rigid and hyper-stable folds that may lack functionality. These approximations contrast with the demonstrated importance of molecular flexibility and motions in a wide range of protein functions. The integration of backbone flexibility and multiple conformational states in CPD, in order to relieve the inaccuracies resulting from these simplifications and to improve design reliability, are attracting increased attention. However, the greatly increased search space that needs to be explored in these extensions defines extremely challenging computational problems. In this review, we outline the principles of CPD and discuss recent effort in algorithmic developments for incorporating molecular flexibility in the design process.

Probing the determinants of the transglycosylation/hydrolysis partition in a retaining α-l-arabinofuranosidase.

The use of retaining glycoside hydrolases as synthetic tools for glycochemistry is highly topical and the focus of considerable research. However, due to the incomplete identification of the molecular determinants of the transglycosylation/hydrolysis partition (t/h), rational engineering of retaining glycoside hydrolases to create transglycosylases remains challenging. Therefore, to understand better the factors that underpin transglycosylation in a GH51 retaining α-l-arabinofuranosidase from Thermobacillus xylanilyticus, the investigation of this enzyme's active site was pursued. Specifically, the properties of two mutants, F26L and L352M, located in the vicinity of the active site are described, using kinetic and 3D structural analyses and molecular dynamics simulations. The results reveal that the presence of L352M in the context of a triple mutant (also containing R69H and N216W) generates changes both in the donor and acceptor subsites, the latter being the result of a domino-like effect. Overall, the mutant R69H-N216W-L352M displays excellent transglycosylation activity (70 % yield, 78 % transfer rate and reduced secondary hydrolysis of the product). In the course of this study, the central role played by the conserved R69 residue was also reaffirmed. The mutation R69H affects both the catalytic nucleophile and the acid/base, including their flexibility, and has a determinant effect on the t/h partition. Finally, the results reveal that increased loop flexibility in the acceptor subsites creates new interactions with the acceptor, in particular with a hydrophobic binding platform composed of N216W, W248 and W302.

Enzymes to unravel bioproducts architecture.

Enzymes are essential and ubiquitous biocatalysts involved in various metabolic pathways and used in many industrial processes. Here, we reframe enzymes not just as biocatalysts transforming bioproducts but also as sensitive probes for exploring the structure and composition of complex bioproducts, like meat tissue, dairy products and plant materials, in both food and non-food bioprocesses. This review details the global strategy and presents the most recent investigations to prepare and use enzymes as relevant probes, with a focus on glycoside-hydrolases involved in plant deconstruction and proteases and lipases involved in food digestion. First, to expand the enzyme repertoire to fit bioproduct complexity, novel enzymes are mined from biodiversity and can be artificially engineered. Enzymes are further characterized by exploring sequence/structure/dynamics/function relationships together with the environmental factors influencing enzyme interactions with their substrates. Then, the most advanced experimental and theoretical approaches developed for exploring bioproducts at various scales (from nanometer to millimeter) using active and inactive enzymes as probes are illustrated. Overall, combining multimodal and multiscale approaches brings a better understanding of native-form or transformed bioproduct architecture and composition, and paves the way to mainstream the use of enzymes as probes.

Positive multistate protein design.

MOTIVATION: Structure-based computational protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. The usual approach considers a single rigid backbone as a target, which ignores backbone flexibility. Multistate design (MSD) allows instead to consider several backbone states simultaneously, defining challenging computational problems. RESULTS: We introduce efficient reductions of positive MSD problems to Cost Function Networks with two different fitness definitions and implement them in the Pompd (Positive Multistate Protein design) software. Pompd is able to identify guaranteed optimal sequences of positive multistate full protein redesign problems and exhaustively enumerate suboptimal sequences close to the MSD optimum. Applied to nuclear magnetic resonance and back-rubbed X-ray structures, we observe that the average energy fitness provides the best sequence recovery. Our method outperforms state-of-the-art guaranteed computational design approaches by orders of magnitudes and can solve MSD problems with sizes previously unreachable with guaranteed algorithms. AVAILABILITY AND IMPLEMENTATION: https://forgemia.inra.fr/thomas.schiex/pompd as documented Open Source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A structural homology approach for computational protein design with flexible backbone.

MOTIVATION: Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. RESULTS: We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. AVAILABILITY AND IMPLEMENTATION: Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variable Neighborhood Search with Cost Function Networks To Solve Large Computational Protein Design Problems.

Computational protein design (CPD) aims to predict amino acid sequences that fold to specific structures and perform desired functions. CPD depends on a rotamer library, an energy function, and an algorithm to search the sequence/conformation space. Variable neighborhood search (VNS) with cost function networks is a powerful framework that can provide tight upper bounds on the global minimum energy. We propose a new CPD heuristic based on VNS in which a subset of the solution space (a "neighborhood") is explored, whose size is gradually increased with a dedicated probabilistic heuristic. The algorithm was tested on 99 protein designs with fixed backbones involving nine proteins from the SH2, SH3, and PDZ families. The number of mutating positions was 20, 30, or all of the amino acids, while the rest of the protein explored side-chain rotamers. VNS was more successful than Monte Carlo (MC), replica-exchange MC, and a heuristic steepest-descent energy minimization, providing solutions with equal or lower best energies in most cases. For complete protein redesign, it gave solutions that were 2.5 to 11.2 kcal/mol lower in energy than those obtained with the other approaches. VNS is implemented in the toulbar2 software. It could be very helpful for large and/or complex design problems.

Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.

Motivation: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Results: Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. Availability and implementation: as open-source Python/C++ code at sourcesup.renater.fr/projects/easy-jayz. Supplementary information: Supplementary data are available at Bioinformatics online.

Engineering of anp efficient mutant of Neisseria polysaccharea amylosucrase for the synthesis of controlled size maltooligosaccharides.

Amylosucrase from Neisseria polysaccharea naturally catalyzes the synthesis of α-1,4 glucans from sucrose. The product profile is quite polydisperse, ranging from soluble chains called maltooligosaccharides to high-molecular weight insoluble amylose. This enzyme was recently subjected to engineering of its active site to enable recognition of non-natural acceptor substrates. Libraries of variants were constructed and screened on sucrose, allowing the identification of a mutant that showed a 6-fold enhanced activity toward sucrose compared to the wild-type enzyme. Furthermore, its product profile was unprecedented, as only soluble maltooligosaccharides of controlled size chains (2

Production of Medium Chain Fatty Acids by Yarrowia lipolytica: Combining Molecular Design and TALEN to Engineer the Fatty Acid Synthase.

Yarrowia lipolytica is a promising organism for the production of lipids of biotechnological interest and particularly for biofuel. In this study, we engineered the key enzyme involved in lipid biosynthesis, the giant multifunctional fatty acid synthase (FAS), to shorten chain length of the synthesized fatty acids. Taking as starting point that the ketoacyl synthase (KS) domain of Yarrowia lipolytica FAS is directly involved in chain length specificity, we used molecular modeling to investigate molecular recognition of palmitic acid (C16 fatty acid) by the KS. This enabled to point out the key role of an isoleucine residue, I1220, from the fatty acid binding site, which could be targeted by mutagenesis. To address this challenge, TALEN (transcription activator-like effector nucleases)-based genome editing technology was applied for the first time to Yarrowia lipolytica and proved to be very efficient for inducing targeted genome modifications. Among the generated FAS mutants, those having a bulky aromatic amino acid residue in place of the native isoleucine at position 1220 led to a significant increase of myristic acid (C14) production compared to parental wild-type KS. Particularly, the best performing mutant, I1220W, accumulates C14 at a level of 11.6% total fatty acids. Overall, this work illustrates how a combination of molecular modeling and genome-editing technology can offer novel opportunities to rationally engineer complex systems for synthetic biology.

Rational design of adjuvants targeting the C-type lectin Mincle.

The advances in subunit vaccines development have intensified the search for potent adjuvants, particularly adjuvants inducing cell-mediated immune responses. Identification of the C-type lectin Mincle as one of the receptors underlying the remarkable immunogenicity of the mycobacterial cell wall, via recognition of trehalose-6,6'-dimycolate (TDM), has opened avenues for the rational design of such molecules. Using a combination of chemical synthesis, biological evaluation, molecular dynamics simulations, and protein mutagenesis, we gained insight into the molecular bases of glycolipid recognition by Mincle. Unexpectedly, the fine structure of the fatty acids was found to play a key role in the binding of a glycolipid to the carbohydrate recognition domain of the lectin. Glucose and mannose esterified at O-6 by a synthetic α-ramified 32-carbon fatty acid showed agonist activity similar to that of TDM, despite their much simpler structure. Moreover, they were seen to stimulate proinflammatory cytokine production in primary human and murine cells in a Mincle-dependent fashion. Finally, they were found to induce strong Th1 and Th17 immune responses in vivo in immunization experiments in mice and conferred protection in a murine model of Mycobacterium tuberculosis infection. Here we describe the rational development of new molecules with powerful adjuvant properties.

Novel product specificity toward erlose and panose exhibited by multisite engineered mutants of amylosucrase.

A computer-aided engineering approach recently enabled to deeply reshape the active site of N. polysaccharea amylosucrase for recognition of non-natural acceptor substrates. Libraries of variants were constructed and screened on sucrose allowing the identification of 17 mutants able to synthesize molecules from sole sucrose, which are not synthesized by the parental wild-type enzyme. Three of the isolated mutants as well as the new products synthesized were characterized in details. Mutants contain between 7 and 11 mutations in the active site and the new molecules were identified as being a sucrose derivative, named erlose (α-d-glucopyranosyl-(1→4)-α-d-glucopyranosyl-(1→2)-ß-d-Fructose), and a new malto-oligosaccharide named panose (α-d-glucopyranosyl-(1→6)-α-d-glucopyranosyl-(1→4)-α-d-Glucose). These product specificities were never reported for none of the amylosucrases characterized to date, nor their engineered variants. Optimization of the production of these trisaccharides of potential interest as sweeteners or prebiotic molecules was carried out. Molecular modelling studies were also performed to shed some light on the molecular factors involved in the novel product specificities of these amylosucrase variants.